SoroushVahidi/scidocs huggingface dataset:
Impact of research on practice in the field of inspections, reviews and walkthroughs: learning from successful industrial uses
(2008)

Software inspections, reviews, and walkthroughs have become a standard process component in many software development domains. Maturity level 3 of the CMM-I requires establishment of peer reviews [12] and substantial sustained improvements in quality and productivity have been reported as a result of using reviews ([16], [21], [22], [27]). The NSF Impact project identifies the degree to which these industrial success cases have been instigated and improved by research in software engineering.
 This research identifies that there is widespread adoption of inspections, reviews or walkthroughs but that companies do not generally exploit their full potential. However there exist sustained industrial success cases with respect to the wide-spread and measurably successful application of them. It also identifies research in software engineering that can be credibly documented as having influenced the industrial success cases. Credible documentation may exist in the form of publications or documented reports by witnesses. Due to the semi-formal nature of inspections, reviews, and walkthroughs, a specific focus is given to empirical research results as motivators for adoption. Through the examination of one detailed case study, it is shown that software engineering research has had a significant impact on practice and that the impact can be traced in this case from research to that practice. The case study chosen provides evidence of both success and failure regarding sustained application in practice.
 Thus the analysis of historic impact chains of research reveals a clear impact of software engineering research on sustained industrial success for inspections, reviews and walkthroughs. More importantly, in impact chains where the empirical results have not been established, we conclude that success has not been achieved or has not been sustained.
 The paper closes with (1) lessons learned for creating the sustained use and impact of semi-formal software engineering processes, (2) a request for researchers and practitioners to further consider how their work can improve the effectiveness of research and practice, and (3) a request to contribute additional success cases and impact factors to the authors database for future enhancements of this paper.
Exploring Deep Learning Methods for Discovering Features in Speech Signals
(2014)

Exploring Deep Learning Methods for discovering features in speech signals. Navdeep Jaitly Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 This thesis makes three main contributions to the area of speech recognition with Deep Neural Network Hidden Markov Models (DNN-HMMs). Firstly, we explore the effectiveness of features learnt from speech databases using Deep Learning for speech recognition. This contrasts with prior works that have largely confined themselves to using traditional features such as Mel Cepstral Coefficients and Mel log filter banks for speech recognition. We start by showing that features learnt on raw signals using Gaussian-ReLU Restricted Boltzmann Machines can achieve accuracy close to that achieved with the best traditional features. These features are, however, learnt using a generative model that ignores domain knowledge. We develop methods to discover features that are endowed with meaningful semantics that are relevant to the domain using capsules. To this end, we extend previous work on transforming autoencoders and propose a new autoencoder with a domain-specific decoder to learn capsules from speech databases. We show that capsule instantiation parameters can be combined with Mel log filter banks to produce improvements in phone recognition on TIMIT. On WSJ the word error rate does not improve, even though we get strong gains in classification accuracy. We speculate this may be because of the mismatched objectives of word error rate over an utterance and frame error rate on the sub-phonetic class for a frame. Secondly, we develop a method for data augmentation in speech datasets. Such methods result in strong gains in object recognition, but have largely been ignored in speech recognition. Our data augmentation encourages the learning of invariance to vocal tract length of speakers. The method is shown to improve the phone error rate on TIMIT and the word error rate on a 14 hour subset of WSJ. Lastly, we develop a method for learning and using a longer range model of targets, conditioned on the input. This method predicts the labels for multiple frames together and uses a geometric average of these predictions during decoding. It produces state of the art results on phone recognition with TIMIT and also produces significant gains on WSJ.
Collaborative Quantization for Cross-Modal Similarity Search
(2016)

Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.
Centrocestus formosanus (Digenea: Heterophyidae) encysted in the freshwater fish, Puntius brevis, from Lao PDR.
(2008)

The metacercariae of Centrocestus formosanus, a minute intestinal trematode of mammals and birds, were detected in the freshwater fish, Puntius brevis, from Vientiane Municipality, Lao PDR. The metacercariae were experimentally fed to mice, and adult flukes were recovered in their small intestines 7 days later. The adult flukes were morphologically characterized by having 32 (rarely 34) circumoral spines arranged in 2 alternative rows, a large bipartite seminal vesicle, an oval-shaped ovary, and an X-shaped excretory bladder. Based on these characters, the adults were identified as Centrocestus formosanus (Nishigori, 1924). The taxonomic significance of C. formosanus, in relation to a closely related species, C. caninus (Leiper, 1913), is briefly discussed. It has been first verified by adult worm recovery that C. formosanus is prevalent in Vientiane areas of Lao PDR, taking the freshwater fish, P. brevis, as a second intermediate host.
A Low-THD Class-D Audio Amplifier With Dual-Level Dual-Phase Carrier Pulsewidth Modulation
(2015)

In this paper, a class-D audio amplifier which combines the advantages of the phase-shifted carrier pulsewidth modulation (PWM) and the multiple-level carrier PWM is proposed with a dual-level dual-phase carrier (DLDPC) PWM. The proposed closed-loop amplifier includes a second-order integrator and a DLDPC triangular wave generator. Two sets of 180° out-of-phase triangular waves are used as carriers, and each set has its respective offset voltage level with nonoverlapping amplitude. By performing the double Fourier analysis, it can be found that the linearity can be enhanced and the distortion can be reduced with the proposed modulation. Experimental results show that the proposed fully differential DLDPC PWM class-D audio amplifier features a total harmonic distortion lower than 0.01% with an output voltage swing of ±5 V.
Interactions between working memory, attention and eye movements.
(2009)

This paper reviews the recent findings on working memory, attention and eye movements. We discuss the research that shows that many phenomena related to visual attention taking place when selecting relevant information from the environment are similar to processes needed to keep information active in working memory. We discuss new data that show that when retrieving information from working memory, people may allocate visual spatial attention to the empty location in space that used to contain the information that has to be retrieved. Moreover, we show that maintaining a location in working memory not only may involve attention rehearsal, but might also recruit the oculomotor system. Recent findings seem to suggest that remembering a location may involve attention-based rehearsal in higher brain areas, while at the same time there is inhibition of specific motor programs at lower brain areas. We discuss the possibility that working memory functions do not reside at a special area in the brain, but emerge from the selective recruitment of brain areas that are typically involved in spatial attention and motor control.
Use of Design Patterns in PHP-Based Web Application Frameworks
(2008)

It is known that design patterns of object-oriented programming are used in the design of Web applications, but there is no sufficient information which data patterns are used, how often they are used, and the level of quality at which they are used. This paper describes the results concerning the use of design patterns in projects which develop PHP-based Web application frameworks. Documentation and source code were analysed for 10 frameworks, finding that design patterns are used in the development of Web applications, but not too much and without much consistency. The results and conclusions can be of use when planning and developing new projects because the existing experience can be taken into account. The paper also offers information which design patterns are not used because they may be artificial or hard-to-use in real projects. Alternatively, developers may simply lack information on the existence of the design patterns.
PHP FRAMEWORK FOR DATABASE MANAGEMENT BASED ON MVC PATTERN
(2011)

PHP is a powerful language to develop dynamic and interactive web applications. One of the defining features of PHP is the ease for developers to connect and manipulate a database. PHP prepares the functions for database manipulation. However, database management is done by the Structure Query Language (SQL). Most novice programmers often have trouble with SQL syntax. In this paper, we present the PHP framework for database management based on the MVC pattern. The MVC pattern is very useful for the architecture of web applications, separating the model, view and controller of a web application. The PHP framework encapsulated, common database operations are INSERT, UPDATE, DELETE and SELECT. Developers will not be required to consider the specific SQL statement syntax, just to call it the method in the model module. In addition, we use White-Box testing for the code verification in the model module. Lastly, a web application example is shown to illustrate the process of the PHP framework.
Patterns of Enterprise Application Architecture
(2002)

protected void doInsert(DomainObject subject, PreparedStatement insertStatement) throws SQLException; class PersonMapper... protected String insertStatement() { return "INSERT INTO people VALUES (?, ?, ?, ?)"; } protected void doInsert( DomainObject abstractSubject, PreparedStatement stmt) throws SQLException { Person subject = (Person) abstractSubject; stmt.setString(2, subject.getLastName()); stmt.setString(3, subject.getFirstName()); stmt.setInt(4, subject.getNumberOfDependents()); } Example: Separating the Finders (Java) To allow domain objects to invoke finder behavior I can use Separated Interface (476) to separate the finder interfaces from the mappers (Figure 10.5 ). I can put these finder interfaces in a separate package that's visible to the domain layer, or, as in this case, I can put them in the domain layer itself. Figure 10.5. Defining a finder interface in the domain package.
The Research of PHP Development Framework Based on MVC Pattern
(2009)

PHP is one of the leading web development languages, however, the development model of existing PHP organizes without a structure, which mixes the code of data access, the processing of business logic , and web presentation layer together, as a relult, it brought about many problems in the web applications , meanwhile, it could not meet the rapid development of web apply any more. In this paper, a implementation of PHP based on MVC design patterns - FDF framework was provided for PHP developers, which can offer a framework for web applications, separate the data, view and control of web applications, afford to achieve loose coupling, thereby enhanced the efficiency, reliability, maintainability and scalability of application development.
Co-regularized least square regression for multi-view multi-class classification
(2016)

Many classification problems involve instances that are unlabeled, multi-view and multi-class. However, few technique has been benchmarked for this complex scenario, with a notable exception that combines co-trained naive bayes (CoT-NB) with BCH coding. In this paper, we benchmark the performance of co-regularized least square regression (CoR-LS) for semi-supervised multi-view multi-class classification. We find it performed consistently and significantly better than CoT-NB over eight data sets at different scales. We also find for CoR-LS identity coding is optimal on large data sets and BCH coding is optimal on small data sets. Optimal scoring, a data-dependent coding scheme, often provides near-optimal performance.
Introduction to Fillers.
(2015)

BACKGROUND
Over the last few years, injectable soft-tissue fillers have become an integral part of cosmetic therapy, with a wide array of products designed to fill lines and folds and revolumize the face.


METHODS
This review describes cosmetic fillers currently approved by the Food and Drug Administration and discusses new agents under investigation for use in the United States.


RESULTS
Because of product refinements over the last few years-greater ease of use and longevity, the flexibility of multiple formulations within one line of products, and the ability to reverse poor clinical outcomes-practitioners have gravitated toward the use of biodegradable agents that stimulate neocollagenesis for sustained aesthetic improvements lasting up to a year or more with minimal side effects. Permanent implants provide long-lasting results but are associated with greater potential risk of complications and require the skilled hand of the experienced injector.


CONCLUSIONS
A variety of biodegradable and nonbiodegradable filling agents are available or under investigation in the United States. Choice of product depends on injector preference and the area to be filled. Although permanent agents offer significant clinical benefits, modern biodegradable fillers are durable and often reversible in the event of adverse effects.
Truncated Importance Sampling for Reinforcement Learning with Experience Replay
(2007)

Reinforcement Learning (RL) is considered here as an adaptation technique of neural controllers of machines. The goal is to make Actor-Critic algorithms require less agent-environment interaction to obtain policies of the same quality, at the cost of additional background computations. We propose to achieve this goal in the spirit of experience replay. An estimation method of improvement direction of a changing policy, based on preceding experience, is essential here. We propose one that uses truncated importance sampling. We derive bounds of bias of that type of estimators and prove that this bias asymptotically vanishes. In the experimental study we apply our approach to the classic ActorCritic and obtain 20-fold increase in speed of learning.
EmEx, a Tool for Automated Emotive Face Recognition Using Convolutional Neural Networks
(2017)

The work described in this paper represents the study and the attempt to make a contribution to one of the most stimulating and promising sectors in the field of emotion recognition, which is health care management. Multidisciplinary studies in artificial intelligence, augmented reality and psychology stressed out the importance of emotions in communication and awareness. The intent is to recognize human emotions, processing images streamed in real-time from a mobile device. The adopted techniques involve the use of open source libraries of visual recognition and machine learning approaches based on convolutional neural networks (CNN).
A Novel Secure Architecture for the Internet of Things
(2011)

The "Internet of Things"(IoT) opens opportunities for devices and softwares to share information on an unprecedented scale. However, such a large interconnected network poses new challenges to system developers and users. In this article, we propose a layered architecture of IoT system. Using this model, we try to identify and assess each layer's challenges. We also discuss several existing technologies that can be used make this architecture secure.
MIMO Performance of Realistic UE Antennas in LTE Scenarios at 750 MHz
(2011)

Multiple-input-multiple-output (MIMO) is a technique to achieve high data rates in mobile communication networks. Simulations are performed at both the antenna level and Long-Term Evolution (LTE) system level to assess the performance of realistic handheld devices with dual antennas at 750 MHz. It is shown that MIMO works very well and gives substantial performance gain in user devices with a quarter-wavelength antenna separation.
Implementation and verification of a generic universal memory controller based on UVM
(2015)

This paper presents a coverage driven constraint random based functional verification method based on the Universal Verification Methodology (UVM) using System Verilog for generic universal memory controller architecture. This universal memory controller is looking forward to improving the performance of the existing memory controllers through a complete integration of the existing memory controllers features in addition of providing novel features. It also reduces the consumed power through providing high power consumption control due to its proposed different power levels supported to fit all power scenarios. While implementing a worthy architecture like the proposed generic universal memory controller, UVM is the best choice to build well-constructed, high controlled and reusable verification environment to efficiently verify it. More than 200 coverage points have been covered to verify the validation of the integrated features which makes the proposed universal memory controller replaces the existing controllers on the scene as it provides all of their powerful features in addition of novel features to control two of the most dominated types of memory; FLASH and DRAM through one memory controller.
Towards Internet of Things : Survey and Future Vision
(2013)

Internet of things is a promising research due to its importance in many commerce, industry, and education applications. Recently, new applications and research challenges in numerous areas of Internet of things are fired. In this paper, we discuss the history of Internet of things, different proposed architectures of Internet of things, research challenges and open problems related to the Internet of things. We also introduce the concept of Internet of things database and discuss about the future vision of Internet of things. These are the manuscript preparation guidelines used as a standard template for all journal submissions. Author must follow these instructions while preparing/modifying these guidelines.
Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning
(2018)

A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task. In this work, we argue that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation but also helps semantic attribute prediction. A novel dictionary learning model is proposed which decomposes the dictionary space into three parts corresponding to semantic, latent discriminative and latent background attributes respectively. Such a joint attribute learning model is then extended by following a multi-task transfer learning framework to address a more challenging unsupervised domain adaptation problem, where annotations are only available on an auxiliary dataset and the target dataset is completely unlabelled. Extensive experiments show that the proposed models, though being linear and thus extremely efficient to compute, produce state-of-the-art results on both zero-shot learning and person re-identification.
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
(2016)

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural networks in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm — the parallel knowledge gradient method. By construction, this method provides the one-step Bayes optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.
The structure of musical preferences: a five-factor model.
(2011)

Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners' affective reactions to excerpts of music from a wide variety of musical genres. The findings from 3 independent studies converged to suggest that there exists a latent 5-factor structure underlying music preferences that is genre free and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as (a) a Mellow factor comprising smooth and relaxing styles; (b) an Unpretentious factor comprising a variety of different styles of sincere and rootsy music such as is often found in country and singer-songwriter genres; (c) a Sophisticated factor that includes classical, operatic, world, and jazz; (d) an Intense factor defined by loud, forceful, and energetic music; and (e) a Contemporary factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and the auditory characteristics of the music.